Self-Driving Car Engineer Capstone project: Driving Carla!
Joseph Januszkiewicz (Team Lead) email@example.com
Darien Martinez firstname.lastname@example.org
Alberto Vigata email@example.com
Moses Gaspard firstname.lastname@example.org
Original README Below
This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. For more information about the project, see the project introduction here.
- Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.
- If using a Virtual Machine to install Ubuntu, use the following configuration as minimum:
- 2 CPU
- 2 GB system memory
- 25 GB of free hard drive space
The Udacity provided virtual machine has ROS and Dataspeed DBW already installed, so you can skip the next two steps if you are using this.
- Follow these instructions to install ROS
- Dataspeed DBW
- Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)
- Download the Udacity Simulator.
Build the docker container
docker build . -t capstone
Run the docker file
docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone
- Clone the project repository
git clone https://github.com/udacity/CarND-Capstone.git
- Install python dependencies
cd CarND-Capstone pip install -r requirements.txt
- Make and run styx
cd ros catkin_make source devel/setup.sh roslaunch launch/styx.launch
- Run the simulator
Real world testing
- Download training bag that was recorded on the Udacity self-driving car (a bag demonstraing the correct predictions in autonomous mode can be found here)
- Unzip the file
- Play the bag file
rosbag play -l traffic_light_bag_files/loop_with_traffic_light.bag
- Launch your project in site mode
cd CarND-Capstone/ros roslaunch launch/site.launch
- Confirm that traffic light detection works on real life images